
'''simple docstring'''





import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / """utils"""))

from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


SCREAMING_SNAKE_CASE     =       get_tests_dir("""fixtures""")






class 							UpperCAmelCase_							(    unittest.TestCase				):

 '''simple docstring'''







 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[int]				       =							mock.Mock()
   UpperCAmelCase__      :					str				       =							5_00
   UpperCAmelCase__      :					Dict				       =							{}
   UpperCAmelCase__      :					Optional[Any]				       =							HTTPError
   UpperCAmelCase__      :					Union[str, Any]				       =							{}

   # Download this model to make sure it's in the cache.
   UpperCAmelCase__      :					List[Any]				       =							WavaVecaFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2" )
   # Under the mock environment we get a 500 error when trying to reach the model.
   with mock.patch("requests.Session.request"			,				return_value=snake_case__ ) as mock_head:
     UpperCAmelCase__      :					Tuple				       =							WavaVecaFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-wav2vec2" )
     # This check we did call the fake head request
     mock_head.assert_called()







 def 							UpperCamelCase  (					self		:  int ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[Any]				       =							WavaVecaFeatureExtractor.from_pretrained(
       "https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json" )






@is_staging_test
class 							UpperCAmelCase_							(    unittest.TestCase				):

 '''simple docstring'''







 @classmethod
 def 							UpperCamelCase  (					cls		:  List[str] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Dict				       =							TOKEN
   HfFolder.save_token(snake_case__ )







 @classmethod
 def 							UpperCamelCase  (					cls		:  List[str] ):



   '''simple docstring'''




   try:
     delete_repo(token=cls._token			,				repo_id="test-feature-extractor" )
   except HTTPError:
     pass

   try:
     delete_repo(token=cls._token			,				repo_id="valid_org/test-feature-extractor-org" )
   except HTTPError:
     pass

   try:
     delete_repo(token=cls._token			,				repo_id="test-dynamic-feature-extractor" )
   except HTTPError:
     pass







 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   UpperCAmelCase__      :					str				       =							WavaVecaFeatureExtractor.from_pretrained(snake_case__ )
   feature_extractor.push_to_hub("test-feature-extractor"			,				use_auth_token=self._token )

   UpperCAmelCase__      :					Optional[Any]				       =							WavaVecaFeatureExtractor.from_pretrained(F"""{USER}/test-feature-extractor""" )
   for k, v in feature_extractor.__dict__.items():
     self.assertEqual(snake_case__			,				getattr(snake_case__			,				snake_case__ ) )

   # Reset repo
   delete_repo(token=self._token			,				repo_id="test-feature-extractor" )

   # Push to hub via save_pretrained
   with tempfile.TemporaryDirectory() as tmp_dir:
     feature_extractor.save_pretrained(
         snake_case__			,				repo_id="test-feature-extractor"			,				push_to_hub=snake_case__			,				use_auth_token=self._token )

   UpperCAmelCase__      :					int				       =							WavaVecaFeatureExtractor.from_pretrained(F"""{USER}/test-feature-extractor""" )
   for k, v in feature_extractor.__dict__.items():
     self.assertEqual(snake_case__			,				getattr(snake_case__			,				snake_case__ ) )







 def 							UpperCamelCase  (					self		:  Union[str, Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							WavaVecaFeatureExtractor.from_pretrained(snake_case__ )
   feature_extractor.push_to_hub("valid_org/test-feature-extractor"			,				use_auth_token=self._token )

   UpperCAmelCase__      :					Optional[int]				       =							WavaVecaFeatureExtractor.from_pretrained("valid_org/test-feature-extractor" )
   for k, v in feature_extractor.__dict__.items():
     self.assertEqual(snake_case__			,				getattr(snake_case__			,				snake_case__ ) )

   # Reset repo
   delete_repo(token=self._token			,				repo_id="valid_org/test-feature-extractor" )

   # Push to hub via save_pretrained
   with tempfile.TemporaryDirectory() as tmp_dir:
     feature_extractor.save_pretrained(
         snake_case__			,				repo_id="valid_org/test-feature-extractor-org"			,				push_to_hub=snake_case__			,				use_auth_token=self._token )

   UpperCAmelCase__      :					str				       =							WavaVecaFeatureExtractor.from_pretrained("valid_org/test-feature-extractor-org" )
   for k, v in feature_extractor.__dict__.items():
     self.assertEqual(snake_case__			,				getattr(snake_case__			,				snake_case__ ) )







 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   CustomFeatureExtractor.register_for_auto_class()
   UpperCAmelCase__      :					Optional[int]				       =							CustomFeatureExtractor.from_pretrained(snake_case__ )

   feature_extractor.push_to_hub("test-dynamic-feature-extractor"			,				use_auth_token=self._token )

   # This has added the proper auto_map field to the config
   self.assertDictEqual(
       feature_extractor.auto_map			,				{"AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor"}			,				)

   UpperCAmelCase__      :					Union[str, Any]				       =							AutoFeatureExtractor.from_pretrained(
       F"""{USER}/test-dynamic-feature-extractor"""			,				trust_remote_code=snake_case__ )
   # Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
   self.assertEqual(new_feature_extractor.__class__.__name__			,				"CustomFeatureExtractor" )




from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm


def 	lowerCamelCase					(	UpperCamelCase     :				np.ndarray			,       UpperCamelCase     :				np.ndarray							)   ->    float:
		return math.sqrt(sum(pow(a - b			,       2							) for a, b in zip(UpperCamelCase			,       UpperCamelCase							)							)							)


def 	lowerCamelCase					(	UpperCamelCase     :				np.ndarray			,       UpperCamelCase     :				np.ndarray							)   ->    list[list[list[float] | float]]:

		if dataset.ndim != value_array.ndim:
				_lowerCamelCase				   =  (
				    'Wrong input data\'s dimensions... '
				    F"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
				)
				raise ValueError(UpperCamelCase							)

		try:
				if dataset.shape[1] != value_array.shape[1]:
						_lowerCamelCase				   =  (
						    'Wrong input data\'s shape... '
						    F"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
						)
						raise ValueError(UpperCamelCase							)
		except IndexError:
				if dataset.ndim != value_array.ndim:
						raise TypeError('Wrong shape'							)

		if dataset.dtype != value_array.dtype:
				_lowerCamelCase				   =  (
				    'Input data have different datatype... '
				    F"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
				)
				raise TypeError(UpperCamelCase							)

		_lowerCamelCase				   =  []

		for value in value_array:
				_lowerCamelCase				   =  euclidean(UpperCamelCase			,       dataset[0]							)
				_lowerCamelCase				   =  dataset[0].tolist()

				for dataset_value in dataset[1:]:
						_lowerCamelCase				   =  euclidean(UpperCamelCase			,       UpperCamelCase							)

						if dist > temp_dist:
								_lowerCamelCase				   =  temp_dist
								_lowerCamelCase				   =  dataset_value.tolist()

				answer.append([vector, dist]							)

		return answer


def 	lowerCamelCase					(	UpperCamelCase     :				np.ndarray			,       UpperCamelCase     :				np.ndarray							)   ->    float:
		return np.dot(UpperCamelCase			,       UpperCamelCase							) / (norm(UpperCamelCase							) * norm(UpperCamelCase							))


if __name__ == "__main__":
				import doctest

				doctest.testmod()